Lead Data Engineer – AI/Machine Learning

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We are looking for a Lead AI Engineer to help shape and drive AI/ML enablement and readiness across the organization.

This role requires strong data engineering fundamentals: you will start hands-on, contributing directly to our data platform and pipelines, while progressively taking on a leading role in defining how the organization builds, deploys, and governs AI/ML capabilities.

Reporting directly to the VP, Head of Data, you will work autonomously to identify gaps, propose solutions, and bring innovative thinking to how our data and AI/ML ecosystem should evolve.

You will partner closely with Data Governance, Data Engineering, and Product stakeholders to define our AI/ML frameworks and MLOps strategy, and to ensure the organization is well-positioned to adopt AI/ML responsibly and at scale.

Key Accountabilities/Deliverables:


* Design, build, and optimize data pipelines, ingestion frameworks, and platform components that support analytics, reporting, and AI/ML use cases.


* Take direct, autonomous ownership of complex engineering initiatives, from technical design through implementation and rollout, with minimal need for oversight.


* Identify and resolve performance, scalability, and reliability issues across the existing data platform.


* Bring innovative, well-reasoned solutions to data engineering problems, proactively identifying gaps and proposing improvements rather than waiting for direction.


* Write clean, well-tested, well-documented code and infrastructure-as-code, maintaining strong engineering hygiene across your work.


* Help define the organization's AI/ML frameworks, evaluating and recommending tools, platforms, and standards for building and deploying AI/ML solutions.


* Build working prototypes that provide immediate value to the engineering teams


* Shape and help implement our MLOps strategy, including approaches to model deployment, monitoring, versioning, and lifecycle management


* Partner in deep, ongoing collaboration with Data Governance to ensure AI/ML frameworks and practices align with data governance, security, and compliance standards.


* Design and advocate for data infrastructure patterns that support AI/ML use cases at scale (e.g., feature stores, curated/governed datasets, streaming access for training and inference).


* Partner with Data Science, Data Engineering, and business stakeholders to assess AI/ML readiness gaps and build a roadmap to close them.


* Act as a subject-matter expert and thought partner to the VP, Head of Data on emerging AI/ML technologies, practices, and industry trends.


* Document AI/ML standards, frameworks, and decisions to support consistent adoption across the organization as the practice matures.


* Act as a senior technical resource for the team, providing guidance on architecture, design patterns, and best practices AI/ML readiness and ML Ops frameworks


* Partner closely with Enterprise Architecture on esta...